Power Generation Nowcasting of the Behind-the-Meter Photovoltaic Systems

Probabilistic analysis of masked loads with aggregated photovoltaic production

In this paper we present a probabilistic analysis framework to estimate behind-the-meter photovoltaic generation in real time. We develop a forward model consisting of a spatiotemporal stochastic process that represents the photovoltaic generation and a stochastic differential equation with jumps that represents the demand. We employ this model to disaggregate the behind-the-meter photovoltaic generation using net load and irradiance measurements.

微型光伏系统对电力配电网的影响与集成

Impact and Integration of Mini Photovoltaic Systems on Electric Power Distribution Grids

本文研究微型光伏系统对低压配电网稳定与控制的影响，通过案例分析和控制策略比较，表明自主逆变器对提供辅助服务至关重要，并强调需要适应性DER控制策略。

A Data-Efficient Approach to Behind-the-Meter Solar Generation Disaggregation

With the emergence of cost effective battery storage and the decline in the solar photovoltaic (PV) levelized cost of energy (LCOE), the number of behind-the-meter solar PV systems is expected to increase steadily. The ability to estimate solar generation from these latent systems is crucial for a range of applications, including distribution system planning and operation, demand response, and non-intrusive load monitoring (NILM). This paper investigates the problem of disaggregating solar generation from smart meter data when historical disaggregated data from the target home is unavailable, …

光伏能源共享：在真实集体自用电系统中的实现与测试

Photovoltaic energy sharing: Implementation and tests on a real collective self-consumption system

本研究探讨了在法国真实集体自用电系统中实现和测试光伏能源共享的不同类型，分析了自用电率和节省情况，并通过不同场景比较了三种共享方式的效果。

在电表上的图机器学习：一个用于微电网边缘智能的光伏功率预测案例研究

On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence

本文研究了图神经网络在微电网边缘智能电表中用于预测光伏功率的方法，介绍了ONNX和ONNX Runtime技术，并通过实际数据集评估了GCN和GraphSAGE模型的性能。

IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) sy…